Table Of Content
This may result in a kind of personalized behavioral medicine in which what gets personalized, and when, is determined through experimental analysis. They were interested in how a school-wide bullying prevention program affected the bullying behaviour of particular problem students. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviours they exhibited toward their peers. They found that the number of aggressive behaviours exhibited by each student dropped shortly after the program was implemented at his or her school.
IX. Chapter 9: Factorial Designs
By comparing the individual's behavior during the A and B phases, researchers can observe whether the intervention has had a significant effect on the behavior in question. Understanding the key features, benefits, and limitations of ABA and ABAB designs will help researchers make informed decisions and select the most suitable design for their study. In the following sections, we will delve deeper into each design, examining their definitions, components, and comparing their similarities and differences.
Treatment
She implemented a simple intervention of using a grip that was recommended by an Occupational Therapist. She decided to test to see if it was indeed the grip that had improved the writing and not just additional practice with writing in school. She removed the pencil grip for one assignment and noted that the legibility of the writing returned to baseline levels. To ensure accurate results, the ABA design typically involves multiple repetitions of the ABA sequence. This helps establish a pattern of behavior change and strengthens the validity of the findings.
Multiple-Baseline Design Across Settings
It should go without saying that we also want our methods to be rigorous, and therefore eliminate or reduce threats to internal validity. We begin with a description of what we consider to be a set of criteria, or ideals, for what research designs should accomplish in attempting to optimize an intervention. Admittedly, these criteria are self-serving in the sense that most of them constitute the strengths of SCDs, but they also apply to other research designs discussed in this volume. Next, we introduce SCDs and how they can be used to optimize treatment using parametric and component analyses.
Multipleprobe
Second, if the intervention produces relatively permanent changes in behavior, then a reversal to pre-intervention conditions may not be possible. Under conditions where behavior may not return to baseline levels when treatment is withdrawn, alternative designs, such as multiple-baseline designs, should be used. In visually inspecting their data, single-subject researchers take several factors into account. One of them is changes in the level of the dependent variable from condition to condition. If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trend, which refers to gradual increases or decreases in the dependent variable across observations.
High quality ABA therapy does exist where the goal is not to “fix” children, make them appear “less autistic”, or change who they are. The goal is to help people learn all that they can so they can live to their potential while embracing their unique, individual personality traits. Such therapies focus on helping kids understand their neurotype and develop coping strategies to help them manage their autistic traits. Such treatments are also focused on assisting people in feeling self-compassion, building confidence, and finding supportive resources in their community.
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For example, the threat known as “testing” refers to the fact that repeated measurement alone may change behavior. To address this, baseline phases need to be sufficiently long, and there must be enough within and/or between participant replications to rule out the effect of testing. Such logic applies to a number of other potential threats (e.g., instrumentation, history, regression to the mean, etc.).
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It is also essential to reinforce your own good behavior as a parent or caregiver, such as taking breaks, maintaining a positive attitude, and being consistent with the ABA program. If your child is responsive to this approach, it may be helpful to incorporate additional visual support at home during downtime. Visual supports encourage children to try new things and help them overcome difficult transitions.
A BCBA gets a new client on her caseload that is witty, cheerful and says some of the brightest comments, however her peers can never hear her because she speaks at one of the quietest speaking levels she has ever heard. She implements an intervention that gradually increases the decibels on a device that turns green when the criteria is met which slowly increases the vocal level of her client’s speaking voice. Her learner contacted natural reinforcement of her peers making reciprocal comments, laughing at her jokes and approaching her in similar situations. Josh decides to implement a break card with a student that will allow them to escape a task or activity within their classroom. He decides to only implement the intervention within the grade level classroom with the learner until it is used independently without prompts.
If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behavior is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is latency, which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible. One of them is changes in the level of the dependent variable from condition to condition. A second factor is trend, which refers to gradual increases or decreases in the dependent variable across observations.
During development, SCDs may obviate some logistical issues in using between-group designs to conduct initial efficacy testing [3, 8]. Specifically, the costs and duration needed to conduct a SCD to establish preliminary efficacy would be considerably lower than traditional randomized designs. Riley and colleagues [8] noted that randomized trials take approximately 5.5 years from the initiation of enrollment to publication, and even longer from the time a grant application is submitted. In addition to establishing whether a treatment works, SCDs have the flexibility to efficiently address which parameters and components are necessary or optimal. In light of traditional methods to establish preliminary efficacy and optimize treatments, Riley and colleagues advocated for “rapid learning research systems.” SCDs are one such system. Whether the data is higher or lower based on a visual inspection of the data; a change in the level implies the treatment introduced had an effect.
In addition to demonstrating an optimal effect, parametric analysis may also reveal boundary conditions. These may be conditions under which an intervention no longer has an effect, or points of diminishing returns in which further increases in some parameter produce no further increases in efficacy. Boundary conditions are critical in establishing generality of an intervention. In most cases, the only way to assess boundary conditions is through experimental, parametric analysis of an individual’s behavior.
Furthermore, in recent years, training in visual analysis has become more formalized and rigorous [44]. Perhaps as a result, Kahng and colleagues found high reliability among visual analysts in judging treatment effects based on analysis of 36 ABAB graphs [45]. The SCD standards recommends four steps and the evaluation of six features of the graphical displays for all participants in a study, which are displayed in Table 3 [31]. As the visual analyst progresses through the steps, he or she also uses the six features to evaluate effects within and across experimental phases.
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